Convergence, Divergence, and Reconvergence in a Feedforward Network Improves Neural Speed and Accuracy.
Convergence, Divergence, and Reconvergence in a Feedforward Network Improves Neural Speed and Accuracy.
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DOI：
10.1016/j.neuron.2015.10.018
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发表时间：
2015-12-02
期刊：
影响因子：
16.2
通讯作者：
Wilson RI
中科院分区：
文献类型：
--
作者：
Jeanne JM;Wilson RI
其中一个被提出的规范电路动机是由大脑采用的前馈网络，其中并行信号收敛，发散，再收敛。在这里，我们研究了果蝇嗅觉系统中具有这种结构的网络。我们关注的是一个肾小球，它的受体神经元以全向全的方式汇聚到六个投射神经元上，然后再汇聚到高阶神经元上。我们发现收敛和再收敛都提高了解码器基于单个神经元的脉冲序列检测刺激的能力。第一种变换实现了平均，提高了峰值检测精度，但没有提高速度；第二个变换实现了巧合检测，它提高了速度，但没有提高峰值精度。在每种情况下，突触后细胞的整合时间和阈值与收敛尖峰序列的统计数据相匹配。
One of the proposed canonical circuit motifs employed by the brain is a feedforward network where parallel signals converge, diverge, and reconverge. Here we investigate a network with this architecture in the Drosophila olfactory system. We focus on a glomerulus whose receptor neurons converge in an all-to-all manner onto six projection neurons that then reconverge onto higher-order neurons. We find that both convergence and reconvergence improve the ability of a decoder to detect a stimulus based on a single neuron’s spike train. The first transformation implements averaging, and it improves peak detection accuracy but not speed; the second transformation implements coincidence detection, and it improves speed but not peak accuracy. In each case, the integration time and threshold of the postsynaptic cell are matched to the statistics of convergent spike trains.